다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

In India, more than 300 million people use Devanagari script for documentation. There has been a significant improvement in the research related to the recognition of printed as well as handwritten Devanagari text in the past few years. State of the art from 1970s of machine printed and handwritten Devanagari optical character recognition (OCR) is discussed in this paper. All feature-extraction techniques as well as training, classification and matching techniques useful for the recognition are discussed in various sections of the paper. An attempt is made to address the most important results reported so far and it is also tried to highlight the beneficial directions of the research till date. Moreover, the paper also contains a comprehensive bibliography of many selected papers appeared in reputed journals and conference proceedings as an aid for the researchers working in the field of Devanagari OCR.

Offline Recognition of Devanagari Script: A Survey

Principal Component Analysis (PCA) and Modular PCA (MPCA) are well known statistical methods for recognition of facial images. But only PCA/MPCA is found to be insufficient to achieve high classification accuracy required for handwritten character recognition application. This is due to the shortcomings of those methods to represent certain local morphometric information present in the character patterns. On the other hand Quad-tree based hierarchically derived Longest-Run (QTLR) features, a type of popularly used topological features for character recognition, miss some global statistical information of the characters. In this paper, we have introduced a new combination of PCA/MPCA and QTLR features for OCR of handwritten numerals. The performance of the designed feature-combination is evaluated on handwritten numerals of five popular scripts of Indian sub-continent, viz., Arabic, Bangla, Devanagari, Latin and Telugu with Support Vector Machine (SVM) based classifier. From the results it has been observed that MPCA+QTLR feature combination outperforms PCA+QTLR feature combination and most other conventional features available in the literature.

A statistical-topological feature combination for recognition of handwritten numerals

In this paper we will try to present a comparative study of non-relational databases and relational databases. We mainly focus our presentation on one implementation of the NoSQL database technology, namely MongoDB, and make a comparison with another implementation of relational databases, namely MySQL, and thus justifying why MongoDB is more efficient than MySQL. We will also present the advantages of using a non-relational database compared to a relational database, integrated in a forum in the field of personal and professional development. The NoSQL database used to develop the forum is MongoDB, and was chosen from a variety of non-relational databases, thanks to some aspects that we will highlight in this article. The database integration in the framework will also be presented.

A comparative study: MongoDB vs. MySQL

In recent years, several research studies have investigated the identification of electronic devices through their physical components and properties, both from a theoretical point of view and through extensive experimental studies. Results have shown that, in many cases, a very high identification accuracy can be obtained by exploiting imperfections and small differences in the electronic components, which are called fingerprints in this context. Part of these studies have focused on a specific category of electronic device, the mobile phone or smartphone, which is usually equipped with components, such as radio frequency front-ends, cameras, micro-electro-mechanical systems, microphones, and speakers that are likely to reveal fingerprints in their digital outputs and then allow the identification of the component and of the mobile phone itself. Keeping the focus on mobile phones, this paper provides a survey of the different techniques for mobile phone identification on the basis of their built-in components. This paper describes the methodology, the classification algorithms, and the types of features that are typically used in literature. Outstanding challenges and research issues are also identified and described, together with an overview of the potential applications of mobile phone fingerprinting. In addition, this paper analyzes the potential privacy risks associated to the tracking of the mobile phone on the basis of its fingerprints and the related mitigation techniques. Finally, it summarizes the main issues and identifies research opportunities and potential future trends for mobile phone fingerprinting.

A Survey of Techniques for the Identification of Mobile Phones Using the Physical Fingerprints of the Built-In Components

New requirements are arising in environments where we have higher volumes of data with high operation rates, agile development and cloud computing. In recent years, a growing number of companies have adopted various types of non- relational database, commonly referred to as NoSQL database. This reflects the growing interactivity of applications which are becoming more networked and social, driving more requests to the database where high-performance NoSQL database such as MongoDB becomes favorable. This paper attempts to use NoSQL database to replace the relational database. It mainly focuses on one of the boosting technology of NoSQL database i.e. MongoDB, and makes a comparison with MySQL and thus justifies why MongoDB is preferred over MySQL. Lastly, a method is proposed to integrate these two types of database by adding a middleware (Metadata) between application layer and database layer.

http://www.ijsrp.org/research-paper-1013/ijsrp-p2255.pdf

SQL Support over MongoDB using Metadata

Recognition of alphabetic characters is a basic need in incorporating intelligence to computers. Machine intelligence involves several aspects among which optical recognition is a tool, which can be integrated to text recognition. To make these aspects effective character recognition with better accuracy is important. However, handwritten character recognition is still a difficult task because of the high variability in the character shapes written by individuals. While large amount of work has been done towards recognition of handwritten English characters relatively less work is reported for the recognition of Indian language scripts. So, we proposed a new elastic image matching (EM) technique based on an eigen-deformation for recognition of offline isolated English uppercase handwritten characters and offline isolated handwritten characters of Devnagari, the most popular script in India. Deformations in handwritten characters have category-dependent tendencies. The estimation and the utilization of such tendencies called eigen-deformations are investigated for the better performance of elastic matching based handwritten character recognition. The eigen-deformations are estimated by the principal component analysis of actual deformations automatically collected by the elastic matching. Typical deformations of each category can be extracted as the eigen-deformations. According to a similarity measure (e.g.: Euclidean, Mahalanobis similarity measures etc.), a prototype matching is done for recognition.

http://robotics.csie.ncku.edu.tw/HCI_Project_2010/P78991121_%E9%84%AD%E8%A9%A0%E4%B9%8B/ICACCC2009%20Handwritten%20character%20recognition%20using%20elastic%20matching%20and%20PCA.pdf

Handwritten character recognition using elastic matching and PCA

Modern technology generates such a huge amount of public and private datasets that its security becomes an inevitable task. Initially the priority was provided for data security for the data of organization's and firm's, but nowadays it is necessary to provide security for personal data as well. So to achieve the data security, it is mandatory to preserve the privacy of personal information for that we use anonymization technique such as generalization, bucketization, multi set-based generalization, one-attribute-per-column slicing, slicing and suppression with slicing are applied to avoid retrieval of data from database. Thus, privacy preservation means to protect the data value and it is used for data mining in order to get the valid and accurate results. These are discussed and successfully analyzed with different parameters such as revealed co-relation quality (linkage property), loss of information, type of data, security (privacy preserved) and membership disclosure in this paper. The analysis shows that suppression with slicing is an innovative technique that preserves the privacy of identity of an individual in a database better than previously mentioned techniques.

Analysis of data security by using anonymization techniques

Single image super-resolution using deep learning techniques has shown very high reconstruction performance over the last few years. We propose a novel three-dimensional convolutional neural network called 3D FSRCNN based on FSRCNN, which reinstates the high-resolution quality of structural MRI. The 3D neural network generates output brain images of high-resolution (HR) from a low-resolution (LR) input image. A simple design ensures less time complexity and high reconstruction quality. The network is trained using T1-weighted structural MRI images from the human connectome project dataset which is a large publicly available brain MRI database.

/pdf/image-super-resolution-for-mri-images-using-3d-faster-super-t4xtpctgup.pdf

Image Super-Resolution for MRI Images using 3D Faster Super-Resolution Convolutional Neural Network architecture

Nowadays in the world of advanced digital computer, tampering and implication of digital images can be easily performed by a tyro with a number of accessible advanced image processing softwares like Adobe Photoshop, Corel Draw etc. Therefore it becomes very challenging for end users to distinguish whether the image is novel or forged. In the fields such as forensics, medical imaging, e-commerce, and industrial photography, legitimacy and integrity of digital images is crucial. This motivates the need for detection tools that are transparent to tampering and can reveal whether an image has been counterfeit just by scrutinizing the counterfeit image. Recently many new methods are presented with improved performances of detection. However there is still place to improve this performance further. Some of the proposed state-of-the-art image tamper detection techniques have been selected for proposal. In the training stage, it extracts the keypoints for every training image using the Mirror invariance feature transform (MIFT), a vector quantization technique maps keypoints from every training image into a unified dimensional histogram vector (bag-of-words) after K-means clustering. This histogram is treated as an input vector for a multiclass SVM to build the training classifier. In the testing stage, the keypoints are extracted and fed into the cluster model to map them into a bag-of-words vector, which is finally fed into the multiclass SVM training classifier to recognize the tampered region.

Robust image forgery localization and recognition in copy-move using bag of features and SVM

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